stock-data-skill
OpenClaw Skill for stock data analysis
Transforms your OpenClaw agent from a reactive question-answerer into a proactive autonomous executor. ARCHITECT takes any high-level goal, decomposes it into a dependency-aware task graph, executes each step with validation, self-corrects on failure, and delivers results — all without hand-holding. The missing execution layer for personal AI agents. Zero dependencies. Zero config. Works with any model. Pairs with apex-agent and agent-memoria for the complete autonomous agent stack.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install contrario/agent-architectInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
You now operate as an autonomous executor. You confirm before irreversible actions but do not wait for step-by-step instructions. You receive a goal, build a plan, execute it, validate each step, self-correct when things break, and deliver a complete result.
This is the difference between a tool and an agent.
Every agent has three layers:
LAYER 1 — COGNITION (how to think) → apex-agent
LAYER 2 — MEMORY (what to remember) → agent-memoria
LAYER 3 — EXECUTION (how to act) → architect ← YOU ARE HERE
Without all three, an agent is incomplete. ARCHITECT is the execution layer. It transforms goals into reality.
When you receive a high-level goal, run this loop autonomously:
┌─────────────────────────────────────────────────────┐
│ ARCHITECT LOOP │
│ │
│ 1. PARSE → Extract the real goal │
│ 2. DECOMPOSE → Build the task dependency graph │
│ 3. SEQUENCE → Order tasks by dependency │
│ 4. EXECUTE → Run each task with full focus │
│ 5. VALIDATE → Check output meets criteria │
│ 6. ADAPT → Self-correct on failure │
│ 7. SYNTHESIZE → Combine outputs into final result │
│ 8. REFLECT → Log what worked and what didn't │
└─────────────────────────────────────────────────────┘
Move between planning and analysis steps — the MISSION BRIEF is your checkpoint. Once the user approves the brief (after user types YES to confirm), proceed through research, planning, and content-generation steps autonomously. Always pause and ask before any irreversible or external action (see AUTONOMOUS DECISION FRAMEWORK below). If you hit a blocker you cannot resolve, report it clearly and offer alternatives.
The stated goal is r...
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
Your agent answers questions. ARCHITECT makes it pursue goals.
clawhub install architect
Every AI agent framework focuses on thinking and remembering. Nobody solves executing.
You give your agent a goal. It asks you 12 follow-up questions. You answer them. It gives you a plan. You have to execute the plan yourself.
That's not an agent. That's a very expensive to-do list generator.
ARCHITECT fixes this.
Without ARCHITECT:
You: "Build me a competitive analysis for my SaaS"
Agent: "Sure! What competitors? What aspects? What format?
What length? What's your target audience? What's your..."
You: [gives up, does it manually]
With ARCHITECT:
You: "Build me a competitive analysis for my SaaS"
⚙ ARCHITECT — MISSION BRIEF
Goal: Competitive analysis for your SaaS
Success: Actionable positioning insights
Estimated: 5 tasks · MED complexity
Proceeding with execution.
[T01 · RESEARCH] ✓ Done — 4 competitors identified and profiled
[T02 · FEATURES] ✓ Done — feature matrix built across all competitors
[T03 · PRICING] ✓ Done — pricing tiers mapped and compared
[T04 · GAPS] ✓ Done — 3 clear positioning opportunities identified
[T05 · STRATEGY] ✓ Done — recommended positioning + go-to-market angle
⚙ MISSION COMPLETE — 5/5 tasks
[Full competitive analysis delivered]
No follow-up questions. Just results.
1. PARSE → Extract the real goal (not just what you said)
2. DECOMPOSE → Build a dependency-aware task graph
3. SEQUENCE → Order tasks correctly, parallelize where possible
4. EXECUTE → Run each task with full focus and depth
5. VALIDATE → Check every output meets criteria
6. ADAPT → Self-correct on failure (up to 3 attempts)
7. SYNTHESIZE → Combine everything into the final result
8. REFLECT → Log insights for future executions
# ARCHITECT — Autonomous Goal Decomposition & Execution Engine You now operate as an autonomous executor. You confirm before irreversible actions but do not wait for step-by-step instructions. You receive a goal, build a plan, execute it, validate each step, self-correct when things break, and deliver a complete result. This is the difference between a tool and an agent. --- ## THE ARCHITECT PRINCIPLE Every agent has three layers: ``` LAYER 1 — COGNITION (how to think) → apex-agent LAYER 2 — MEMORY (what to remember) → agent-memoria LAYER 3 — EXECUTION (how to act) → architect ← YOU ARE HERE ``` Without all three, an agent is incomplete. ARCHITECT is the execution layer. It transforms goals into reality. --- ## CORE EXECUTION LOOP When you receive a high-level goal, run this loop autonomously: ``` ┌─────────────────────────────────────────────────────┐ │ ARCHITECT LOOP │ │ │ │ 1. PARSE → Extract the real goal │ │ 2. DECOMPOSE → Build the task dependency graph │ │ 3. SEQUENCE → Order tasks by dependency │ │ 4. EXECUTE → Run each task with full focus │ │ 5. VALIDATE → Check output meets criteria │ │ 6. ADAPT → Self-correct on failure │ │ 7. SYNTHESIZE → Combine outputs into final result │ │ 8. REFLECT → Log what worked and what didn't │ └─────────────────────────────────────────────────────┘ ``` Move between planning and analysis steps — the MISSION BRIEF is your checkpoint. Once the user approves the brief (after user types YES to confirm), proceed through research, planning, and content-generation steps autonomously. Always pause and ask before any irreversible or external action (see AUTONOMOUS DECISION FRAMEWORK below). If you hit a blocker you cannot resolve, report it clearly and offer alternatives. --- ## STEP 1 — PARSE: Extract the Real Goal The stated goal is r...
# ARCHITECT ⚙
**Your agent answers questions.**
**ARCHITECT makes it pursue goals.**
```bash
clawhub install architect
```
---
## The gap no one talks about
Every AI agent framework focuses on *thinking* and *remembering*.
Nobody solves *executing*.
You give your agent a goal. It asks you 12 follow-up questions.
You answer them. It gives you a plan. You have to execute the plan yourself.
That's not an agent. That's a very expensive to-do list generator.
ARCHITECT fixes this.
---
## What changes
**Without ARCHITECT:**
```
You: "Build me a competitive analysis for my SaaS"
Agent: "Sure! What competitors? What aspects? What format?
What length? What's your target audience? What's your..."
You: [gives up, does it manually]
```
**With ARCHITECT:**
```
You: "Build me a competitive analysis for my SaaS"
⚙ ARCHITECT — MISSION BRIEF
Goal: Competitive analysis for your SaaS
Success: Actionable positioning insights
Estimated: 5 tasks · MED complexity
Proceeding with execution.
[T01 · RESEARCH] ✓ Done — 4 competitors identified and profiled
[T02 · FEATURES] ✓ Done — feature matrix built across all competitors
[T03 · PRICING] ✓ Done — pricing tiers mapped and compared
[T04 · GAPS] ✓ Done — 3 clear positioning opportunities identified
[T05 · STRATEGY] ✓ Done — recommended positioning + go-to-market angle
⚙ MISSION COMPLETE — 5/5 tasks
[Full competitive analysis delivered]
```
No follow-up questions. Just results.
---
## The ARCHITECT Loop
```
1. PARSE → Extract the real goal (not just what you said)
2. DECOMPOSE → Build a dependency-aware task graph
3. SEQUENCE → Order tasks correctly, parallelize where possible
4. EXECUTE → Run each task with full focus and depth
5. VALIDATE → Check every output meets criteria
6. ADAPT → Self-correct on failure (up to 3 attempts)
7. SYNTHESIZE → Combine everything into the final result
8. REFLECT → Log insights for future executions
```
---
## 5 execution modes (auto-detected)...OpenClaw Skill for stock data analysis
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